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Lucene (Concluded) ‏

Lucene (Concluded) ‏. Lucene. Lucene Index Structure.

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Lucene (Concluded) ‏

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  1. Lucene (Concluded)‏

  2. Lucene Lucene Index Structure The core elements of such an index are segments, documents, fields, and terms. Every index consists of one or more segments. Each segment contains one or more documents. Each document has one or more fields, and each field contains one or more terms.

  3. Lucene Each Term Each term is a pair of Strings representing a field name and a value. A segment consists of a series of files. The exact number of files that constitute each segment varies from index to index, and depends on the number of fields that the index contains. All files belonging to the same segment share a common prefix and differ in the suffix. You can think of a segment as a sub-index, although each segment is not a fully-independent index.

  4. Lucene An Index Consisting of a Single Segment -rw-rw-r-- 1 otis otis 4 Nov 22 22:43 deletable -rw-rw-r-- 1 otis otis 1000000 Nov 22 22:43 _lfyc.f1 -rw-rw-r-- 1 otis otis 1000000 Nov 22 22:43 _lfyc.f2 -rw-rw-r-- 1 otis otis 31030502 Nov 22 22:28 _lfyc.fdt -rw-rw-r-- 1 otis otis 8000000 Nov 22 22:28 _lfyc.fdx -rw-rw-r-- 1 otis otis 16 Nov 22 22:28 _lfyc.fnm -rw-rw-r-- 1 otis otis 1253701335 Nov 22 22:43 _lfyc.frq -rw-rw-r-- 1 otis otis 1871279328 Nov 22 22:43 _lfyc.prx -rw-rw-r-- 1 otis otis 14122 Nov 22 22:43 _lfyc.tii -rw-rw-r-- 1 otis otis 1082950 Nov 22 22:43 _lfyc.tis -rw-rw-r-- 1 otis otis 18 Nov 22 22:43 segments

  5. Lucene Understanding the Previous Example Note that all files that belong to this segment start with a common prefix: _lfyc. Because this index contains two fields, you will notice two files with the fN suffix, where N is a number. If this index had three fields, a file named _lfyc.f3 would also be present in the index directory. The number of segments in an index is fixed once the index is fully built, but it varies while indexing is in progress. Lucene adds segments as new documents are added to the index, and merges segments every so often. In the next section we will learn how to control creation and merging of segments in order to improve indexing speed.

  6. Lucene Indexing Speed Factors If you are using Lucene in a non-trivial application, you will want to ensure optimal indexing performance. The bottleneck of a typical text-indexing application is the process of writing index files onto a disk. Therefore, we need to instruct Lucene to be smart about adding and merging segments while indexing documents. When new documents are added to a Lucene index, they are initially stored in memory instead of being immediately written to the disk. This is done for performance reasons. The simplest way to improve Lucene's indexing performance is to adjust the value of IndexWriter's mergeFactor instance variable.

  7. Lucene Merge Factor This value tells Lucene how many documents to store in memory before writing them to the disk, as well as how often to merge multiple segments together. With the default value of 10, Lucene will store 10 documents in memory before writing them to a single segment on the disk. The mergeFactor value of 10 also means that once the number of segments on the disk has reached the power of 10, Lucene will merge these segments into a single segment.

  8. Lucene MergeFactor of 10 For instance, if we set mergeFactor to 10, a new segment will be created on the disk for every 10 documents added to the index. When the 10th segment of size 10 is added, all 10 will be merged into a single segment of size 100. When 10 such segments of size 100 have been added, they will be merged into a single segment containing 1000 documents, and so on. Therefore, at any time, there will be no more than 9 segments in each power of 10 index size.

  9. Lucene MaxMergeDocs The exception noted earlier has to do with another IndexWriter instance variable: maxMergeDocs. While merging segments, Lucene will ensure that no segment with more than maxMergeDocs is created. For instance, if we set maxMergeDocs to 1000, when we add the 10,000th document, instead of merging multiple segments into a single segment of size 10,000, Lucene will create a 10th segment of size 1000, and keep adding segments of size 1000 for every 1000 documents added. The default value of maxMergeDocs is Integer#MAX_VALUE. In my experience, one rarely needs to change this value.

  10. Lucene Higher Merge Factor Now that I have explained how mergeFactor and maxMergeDocs work, you can see that using a higher value for mergeFactor will cause Lucene to use more RAM, but will let Lucene write data to disk less frequently, which will speed up the indexing process. A smaller mergeFactor will use less memory and will cause the index to be updated more frequently, which will make it more up-to-date, but will also slow down the indexing process. Similarly, a larger maxMergeDocs is better suited for batch indexing, and a smaller maxMergeDocs is better for more interactive indexing.

  11. Lucene Merge Factor Example To get a better feel for how different values of mergeFactor and maxMergeDocs affect indexing speed, take a look at the IndexTuningDemo class on the next slide. This class takes three arguments on the command line: the total number of documents to add to the index, the value to use for mergeFactor, and the value to use for maxMergeDocs. All three arguments must be specified, must be integers, and must be in this order. In order to keep the code short and clean, there are no checks for improper usage.

  12. Lucene Code Example public class IndexTuningDemo { public static void main(String[] args) throws Exception { int docsInIndex = Integer.parseInt(args[0]); // create an index called 'index' in a temporary directory String indexDir = System.getProperty("java.io.tmpdir", "tmp") + System.getProperty("file.separator") + "index"; Analyzer analyzer = new StopAnalyzer(); IndexWriter writer = new IndexWriter(indexDir, analyzer, true); // set variables that affect speed of indexing writer.mergeFactor = Integer.parseInt(args[1]); writer.maxMergeDocs = Integer.parseInt(args[2]); long startTime = System.currentTimeMillis(); for (int i = 0; i < docsInIndex; i++)‏ { Document doc = new Document(); doc.add(Field.Text("fieldname", "Bibamus, moriendum est")); writer.addDocument(doc); } writer.close(); long stopTime = System.currentTimeMillis(); System.out.println("Total time: " + (stopTime - startTime) + " ms"); } }

  13. Lucene Here are some results: prompt> time java IndexTuningDemo 100000 10 1000000 Total time: 410092 ms real 6m51.801s user 5m30.000s sys 0m45.280s prompt> time java IndexTuningDemo 100000 1000 100000 Total time: 249791 ms real 4m11.470s user 3m46.330s sys 0m3.660s As you can see, both invocations created an index with 100,000 documents, but the first one took much longer to complete. That is because it used the default mergeFactor of 10, which caused Lucene to write documents to the disk more often than the mergeFactor of 1000 used in the second invocation.

  14. Lucene Affect the Number of File Descriptors Note that while these two variables can help improve indexing performance, they also affect the number of file descriptors that Lucene uses, and can therefore cause the "Too many open files" exception. If you get this error, you should first see if you can optimize the index, as will be described shortly. Optimization may help indexes that contain more than one segment. If optimizing the index does not solve the problem, you could try increasing the maximum number of open files allowed on your computer. This is usually done at the operating-system level and varies from OS to OS. If you are using Lucene on a computer that uses a flavor of the UNIX OS, you can see the maximum number of open files allowed from the command line.

  15. Lucene ulimit Under bash, you can see the current settings with the built-in ulimit command: prompt> ulimit -n Under tcsh, the equivalent is: prompt> limit descriptors To change the value under bash, use this: prompt> ulimit -n <max number of open files here> Under tcsh, use the following: prompt> limit descriptors <max number of open files here>

  16. Lucene Estimating Maximum Open Files To estimate a setting for the maximum number of open files allowed while indexing, keep in mind that the maximum number of files Lucene will open is (1 + mergeFactor) * FilesPerSegment. For instance, with a default mergeFactor of 10 and an index of 1 million documents, Lucene will require 110 open files on an unoptimized index. When IndexWrite's optimize() method is called, all segments are merged into a single segment, which minimizes the number of open files that Lucene needs.

  17. Lucene In-Memory Indexing In the previous section, I mentioned that new documents added to an index are stored in memory before being written to the disk. You also saw how to control the rate at which this is done via IndexWriter's instance variables. The Lucene distribution contains the RAMDirectory class, which gives even more control over this process. This class implements the Directory interface, just like FSDirectory does, but stores indexed documents in memory, while FSDirectory stores them on disk. Because RAMDirectory does not write anything to the disk, it is faster than FSDirectory. However, since computers usually come with less RAM than hard disk space, RAMDirectory is not suitable for very large indices.

  18. Lucene How to use RAMDirectory to improve the indexing speed public class MemoryVsDisk { public static void main(String[] args) throws Exception { int docsInIndex = Integer.parseInt(args[0]); // create an index called 'index' in a temporary directory String indexDir = System.getProperty("java.io.tmpdir", "tmp") + System.getProperty("file.separator") + "index"; Analyzer analyzer = new StopAnalyzer(); long startTime = System.currentTimeMillis(); if ("-r".equalsIgnoreCase(args[3]))‏ { // if -r argument was specified, use RAMDirectory RAMDirectory ramDir = new RAMDirectory(); IndexWriter ramWriter = new IndexWriter(ramDir, analyzer, true); addDocs(ramWriter, docsInIndex); IndexWriter fsWriter = new IndexWriter(indexDir, analyzer, true); fsWriter.addIndexes(new Directory[] { ramDir }); ramWriter.close(); fsWriter.close(); } else { // create an index using FSDirectory IndexWriter fsWriter = new IndexWriter(indexDir, analyzer, true); fsWriter.mergeFactor = Integer.parseInt(args[1]); fsWriter.maxMergeDocs = Integer.parseInt(args[2]); addDocs(fsWriter, docsInIndex); fsWriter.close(); }

  19. Lucene Example (Concluded)‏ long stopTime = System.currentTimeMillis(); System.out.println("Total time: " + (stopTime - startTime) + " ms"); } private static void addDocs(IndexWriter writer, int docsInIndex)‏ throws IOException { for (int i = 0; i < docsInIndex; i++)‏ { Document doc = new Document(); doc.add(Field.Text("fieldname", "Bibamus, moriendum est")); writer.addDocument(doc); } } }

  20. Lucene Results To create an index with 10,000 documents and only use FSDirectory, use this: prompt> time java MemoryVsDisk 10000 10 100000 -f Total time: 41380 ms real 0m42.739s user 0m36.750s sys 0m4.180s To create the index of the same size but do it faster, with RAMDirectory, call MemoryVsDisk as follows: prompt> time java MemoryVsDisk 10000 10 100000 -r Total time: 27325 ms real 0m28.695s user 0m27.920s sys 0m0.610s However, note that you can achieve the same, or even better, performance by choosing a more suitable value for mergeFactor: prompt> time java MemoryVsDisk 10000 1000 100000 -f Total time: 24724 ms real 0m26.108s user 0m25.280s sys 0m0.620s Be careful, however, when tuning mergeFactor. A value that requires more memory than your JVM can access may cause the java.lang.OutOfMemoryError error.

  21. Lucene JVM Memory Finally, do not forget that you can greatly influence the performance of any Java application by giving the JVM more memory to work with: prompt> time java -Xmx300MB -Xms200MB MemoryVsDisk 10000 10 100000 -r Total time: 15166 ms real 0m17.311s user 0m15.400s sys 0m1.590ss

  22. Lucene Merging Indices If you want to improve indexing performance with Lucene, and manipulating IndexWriter's mergeFactor and maxMergeDocs prove insufficient, you can use RAMDirectory to create in-memory indices. You could create a multi-threaded indexing application that uses multiple RAMDirectory-based indices in parallel, one in each thread, and merges them into a single index on the disk using IndexWriter's addIndexes(Directory[]) method. Taking this idea further, a sophisticated indexing application could even create in-memory indices on multiple computers in parallel. To make full use of this approach, one needs to ensure that the thread that performs the actual indexing on the disk is never idle, as that translates to wasted time.

  23. Lucene Indexing in Multi-Threaded Environments While multiple threads or processes can search (i.e. read) a single Lucene index simultaneously, only a single thread or process is allowed to modify (write) an index at a time. If your indexing application uses multiple indexing threads that are adding documents to the same index, you must serialize their calls to the IndexWriter.addDocument(Document) method. Leaving these calls unserialized may cause threads to get in each other's way and modify the index in unwanted ways, causing Lucene to throw exceptions. In addition, to prevent misuse, Lucene uses file-based locks in order to stop multiple threads or processes from creating IndexWriters with the same index directory at the same time.

  24. Lucene Optimizing Indices To optimize an index, one has to call optimize() on an IndexWriter instance. When this happens, all in-memory documents are flushed to the disk and all index segments are merged into a single segment, reducing the number of files that make up the index. However, optimizing an index does not help improve indexing performance. As a matter of fact, optimizing an index during the indexing process will only slow things down. Despite this, optimizing may sometimes be necessary in order to keep the number of open files under control. For instance, optimizing an index during the indexing process may be needed in situations where searching and indexing happen concurrently, since both processes keep their own set of open files. A good rule of thumb is that if more documents will be added to the index soon, you should avoid calling optimize(). If, on the other hand, you know that the index will not be modified for a while, and the index will only be searched, you should optimize it. That will reduce the number of segments (files on the disk), and consequently improve search performance--the fewer files Lucene has to open while searching, the faster the search.

  25. Lucene Lucene's Disjunctive Search Algorithm public class IndexOptimizeDemo { public static void main(String[] args) throws Exception { // create an index called 'index' in a temporary directory String indexDir = System.getProperty("java.io.tmpdir", "tmp") + System.getProperty("file.separator") + "index"; Analyzer analyzer = new StopAnalyzer(); IndexWriter writer = new IndexWriter(indexDir, analyzer, true); for (int i = 0; i < 15; i++)‏ { Document doc = new Document(); doc.add(Field.Text("fieldname", "Bibamus, moriendum est")); writer.addDocument(doc); } if ("-o".equalsIgnoreCase(args[0]))‏ { System.out.println("Optimizing the index..."); writer.optimize(); } writer.close(); } }

  26. Lucene Optimized Index As you can see from the class Javadoc and code, the created index will be optimized only if -o command line argument is used. To create an unoptimized index with this class, use this: prompt> java IndexOptimizeDemo -n -rw-rw-r-- 1 otis otis 10 Feb 18 23:50 _a.f1 -rw-rw-r-- 1 otis otis 260 Feb 18 23:50 _a.fdt -rw-rw-r-- 1 otis otis 80 Feb 18 23:50 _a.fdx -rw-rw-r-- 1 otis otis 14 Feb 18 23:50 _a.fnm -rw-rw-r-- 1 otis otis 30 Feb 18 23:50 _a.frq -rw-rw-r-- 1 otis otis 30 Feb 18 23:50 _a.prx -rw-rw-r-- 1 otis otis 11 Feb 18 23:50 _a.tii -rw-rw-r-- 1 otis otis 41 Feb 18 23:50 _a.tis -rw-rw-r-- 1 otis otis 4 Feb 18 23:50 deletable -rw-rw-r-- 1 otis otis 5 Feb 18 23:50 _g.f1 -rw-rw-r-- 1 otis otis 130 Feb 18 23:50 _g.fdt -rw-rw-r-- 1 otis otis 40 Feb 18 23:50 _g.fdx -rw-rw-r-- 1 otis otis 14 Feb 18 23:50 _g.fnm -rw-rw-r-- 1 otis otis 15 Feb 18 23:50 _g.frq -rw-rw-r-- 1 otis otis 15 Feb 18 23:50 _g.prx -rw-rw-r-- 1 otis otis 11 Feb 18 23:50 _g.tii -rw-rw-r-- 1 otis otis 41 Feb 18 23:50 _g.tis -rw-rw-r-- 1 otis otis 22 Feb 18 23:50 segments

  27. Lucene Optimized Index This index contains two segments. To create a fully-optimized index, call this class with -o command line argument: prompt> java IndexOptimizeDemo -o -rw-rw-r-- 1 otis otis 4 Feb 18 23:50 deletable -rw-rw-r-- 1 otis otis 15 Feb 18 23:50 _h.f1 -rw-rw-r-- 1 otis otis 390 Feb 18 23:50 _h.fdt -rw-rw-r-- 1 otis otis 120 Feb 18 23:50 _h.fdx -rw-rw-r-- 1 otis otis 14 Feb 18 23:50 _h.fnm -rw-rw-r-- 1 otis otis 45 Feb 18 23:50 _h.frq -rw-rw-r-- 1 otis otis 45 Feb 18 23:50 _h.prx -rw-rw-r-- 1 otis otis 11 Feb 18 23:50 _h.tii -rw-rw-r-- 1 otis otis 41 Feb 18 23:50 _h.tis -rw-rw-r-- 1 otis otis 15 Feb 18 23:50 segments

  28. Lucene Conclusion This article has discussed the basic structure of a Lucene index and has demonstrated a few techniques for improving indexing performance. You also learned about potential problems with indexing in multi-threaded environments, about what it means to optimize an index, and how this affects indexing. This knowledge should allow you to gain more control over Lucene's indexing process to improve its performance. The next article will examine Lucene's text-searching capabilities.

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